Sensing and modeling humans are longstanding problems for both computer vision and computer graphics research communities, which serve as the fundamental technology for a myriad of applications such as animation, gaming, augmented, and virtual reality. With the advent of deep learning, significant progress has been made alongside the introduction of large-scale datasets in human-centric sensing and modeling [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. In this work, we present HuMMan, a comprehensive human dataset consisting of 1000 human subjects, captured in total 400k sequences and 60M frames. More importantly, HuMMan features four main properties listed below.
